Trigger-based data collection system

ABSTRACT

A system for collecting and communicating data associated with at least one of a plurality of machines includes a sensor. The sensor is configured to monitor an operational condition of the at least one associated machine and provide signals indicative of the operational condition. The system includes at least one local data system, including a processor, associated with the machine and configured to receive signals from the sensor, detect an event affecting the at least one machine based on the signals received from the sensor, and communicate data relevant to the detected event to a location remote from the at least one machine. A central data system including a processor and configured to receive the data relevant to the detected event and communicate with other machines of the plurality of much such that the other machines communicate data relevant to the detected event to the central data system is included.

TECHNICAL FIELD

This disclosure relates generally to systems and methods monitoringoperation and fault conditions of a machine, and more specifically, tosystems and methods for collecting and communicating data associatedwith a fleet of machines in response to a triggering event.

BACKGROUND

Machine downtime suffered as a result of a fault condition in a machinesuch as, for example, a locomotive, can be costly, so efficientdiagnostics systems are desirable to minimize repair time. In complexmachines with multiple subsystems, it may be difficult to determinewhich subsystem is suffering from a fault or the particular cause ofthat fault without extensive analysis of the affected machine.

For maintenance and diagnostic purposes, it may be desirable to collectdata relating to a machine during real-time operation for laterretrieval. In some diagnostic systems, the data collected often includesmuch more information than what may be desired for a specific purpose orapplication. However, the data collected may be useful for maintenanceof a fleet of machines, in addition to the particular machine from whichthe data is collected.

One solution for monitoring a locomotive is described in U.S. Pat. No.6,487,478 B1 (“the '478 patent”). The '478 patent is directed to anon-board monitor for a railroad locomotive that interfaces with thecontroller subsystems of the locomotive to collect parametricperformance data. The specific data to be collected and the collectionintervals are defined at a remote service center and transmitted to theon-board monitor. The on-board monitor also includes the capability tocollect additional data or collect data more frequently in response tothe results of certain triggering events.

Although the system and method disclosed in the '478 patent may monitorand report operational data of a machine, the system and methoddisclosed may still suffer from a number of possible drawbacks. Forexample, the system and method disclosed in the '478 patent onlycollects information from the particular locomotive that suffered thefault. When machines in a fleet have similar operating conditions,collecting data from multiple machines may lead to faster and moreaccurate fault identification. Furthermore, data collection from a fleetof machines may result in more quickly recognizing a common problemabout the machines, such that similar faults can be prevented in theremainder of the machines in the fleet. Additionally, the system andmethod disclosed in the '478 patent, in response to a fault trigger or arequest from another system, transmits all the data collected from itsmachine. In complex machines such as locomotives, this may result intransmission of a significantly large amount of data, a large portion ofwhich may be wholly unrelated to the fault condition that triggered thedata collection or transmission. When a particular event triggers afault condition, it may be preferable to send only relevant data todecrease the time and cost of data analysis.

The presently disclosed systems and methods may be directed tomitigating or overcoming one or more of the possible drawbacks set forthabove and/or other problems in the art.

SUMMARY

According to one aspect, the present disclosure is directed to a systemfor collecting and communicating data associated with at least one of aplurality of machines and may include at least one sensor associatedwith at least one of the plurality of machines. The at least one sensormay be configured to monitor an operational condition of the at leastone machine and provide signals indicative of the operational condition.The system may also include at least one local data system associatedwith the at least one machine. The at least one local data system mayinclude a processor and may be configured to receive signals from the atleast one sensor and detect an event affecting the at least one machinebased on the signals received from the at least one sensor. The at leastone local data system may also be configured to communicate datarelevant to the detected event to a location remote from the at leastone machine. The system may also include a central data system. Thecentral data system may include a processor and be configured to receivethe data relevant to the detected event and communicate with othermachines of the plurality such that the other machines communicate datarelevant to the detected event to the central data system.

In accordance with another aspect, the present disclosure is directed toa processor-implemented method for collecting data from at least onemachine. The method may include detecting via a first processor an eventassociated with an affected machine of the at least one machines anddetermining a relevant data portion of machine data based on the event.The method may also include commanding the affected machine to collectevent-specific data and receiving via a second processor theevent-specific data and the relevant data portion from the affectedmachine.

According to another aspect, the present disclosure is directed to amachine fleet. The machine fleet may include a plurality of machines anda data collection system. The data collection system may include atleast one sensor associated with at least one of the plurality ofmachines. The at least one sensor may be configured to monitor anoperational condition of the at least one machine and provide signalsindicative of the operational condition. The data collection system mayalso include at least one local data system associated with the at leastone machine. The at least one local data system may include a processorand may be configured to receive signals from the at least one sensorand detect an event affecting the at least one machine based on thesignals received from the at least one sensor. The at least one localdata system may also be configured to communicate data relevant to thedetected event to a location remote from the at least one machine. Thedata collection system may also include a central data system. Thecentral data system may include a processor and be configured to receivethe data relevant to the detected event and communicate with othermachines of the plurality such that the other machines communicate datarelevant to the detected event to the central data system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic depiction of an exemplary machine fleet.

FIG. 2 is a block diagram of an exemplary data collection system.

FIG. 3 is a flowchart of an exemplary method of collecting data from atleast one machine.

DETAILED DESCRIPTION

FIG. 1 shows an exemplary machine fleet 100 in which systems and methodsfor data collection may be implemented consistent with the disclosedembodiments. Machine fleet 100 may include any group of machines 110defined by shared or similar characteristics. According to someembodiments, each machine 110 of machine fleet 100 may be the same typeor the same model of machine. For example, the three machines 110comprising the exemplary machine fleet 100 shown in FIG. 1 are alllocomotives. Machine fleet 100 may include other types of machines,including but not limited to fixed engine systems, constructionmachines, commercial machines, and marine-based machines, that mayincorporate the systems and methods for data collection consistent withthe embodiments disclosed herein.

A machine fleet 100 may be defined by shared or similar characteristicsamong the plurality of machines 110 of machine fleet 100. It may bedesirable to define machine fleet 100 based on a common characteristicthat makes machines 110 of machine fleet 100 particularly likely toexperience common faults or malfunctions. For example, machines 110 thatall work in similar environmental conditions, such as, for example,extremely hot temperatures or windy, dusty climates, are likely toexperience similar operating conditions and suffer from faults relatedto those conditions. Therefore, it may be desirable to collectoperational data from similar machines 110 or machines 110 operating insimilar conditions to identify the cause of possible machine faults,thereby reducing downtime and providing data helpful to preventingsimilar faults among other machines 110 in a particular machine fleet100.

According to some embodiments, machines 110 of a particular machinefleet 100 may also be defined by other common characteristics. Forexample, machine fleet 100 may be a locomotive consist in which eachmachine 110 is a locomotive, and all machines 110 are connected togetherto form a train. According to some embodiments, machines 110 may begrouped in a particular machine fleet 100 by a similar type of load eachmachine 110 carries. For example, machine fleet 100 may include aplurality of locomotives each pulling passenger cars. According to someembodiments, machine fleet 100 may be defined by a common geographiclocation of each machine 110. For example, machines 110 of a particularmachine fleet 100 may all be operating at a single worksite.Alternatively or additionally, machines 110 of a particular machinefleet 100 may all be operating in a certain geographic area, such aswithin a predefined radius of an identified location, or within acertain geographic region.

According to some embodiments, machines 110 of a particular machinefleet 100 may share similar working conditions. For example, machines110 of a particular machine fleet 100 may all work at construction sitesthat present similar problems, such as, for example, rocky soil.Alternatively or additionally, machines 110 of a particular machinefleet 100 may all have similar purposes. For example, machines 110 of aparticular machine fleet 100 may be of different types yet all be usedto move heavy loads. It will be apparent that it may be beneficial tocategorize machines 110 in a particular machine fleet 100 by one or morecharacteristics, including but not limited to those discussed above, inorder to streamline troubleshooting and share useful operatinginformation among machines 110 of the particular machine fleet 100.

Machine fleet 100 may also include a data collection system 115, asillustrated in FIG. 2. Data collection system 115 may gather specificmachine data from a larger pool of data being collected in response to atriggering event or condition. For example, a triggering event mayinclude the fault of a subsystem of one or more of machines 110.Alternatively, the event may include the fault of machine 110.Alternatively or additionally, the triggering event may include atemperature rising above a threshold limit in one or more of machines110. The triggering event may be based on a sensor reading of one ormore sensors associated with machines 110. According to someembodiments, the triggering event may be a communication from a machineoperator of a fault condition or breakdown. For example, an operator ofmachine 110 may send a signal to data collection system 115 indicativeof a fault.

Machine data that data collection system 115 may collect includes twotypes of information. Machines 110 may continuously collect data fromvarious subsystems and sensors within machine. To analyze machine 110 inresponse to a triggering event, it may not be necessary for datacollection system 115 to provide this large amount of data from theaffected machine 110 or other machines 110 in fleet 100. Instead, datacollection system 115 may identify which portions of the total data isrelevant. This first data set, which may include historical data thatpredates the occurrence of the triggering event, is “relevant data.”Data collection system 115 may want additional data collected inresponse to a triggering event. For example, data collection system 115may want data collected for a period of time after the occurrence of theevent. This type of data, measured in response to a triggering event, is“event-specific data.”

Data collection system 115 may include a plurality of sensors 120. Eachsensor 120 may be configured to monitor a particular operationalcondition of a machine 110. For example, each machine 110 may includeone or more sensors 120 configured to monitor the temperature at variouslocations of associated machine 110. Additionally or alternatively,machine 110 may include sensors 120 to monitor the functionality of oneor more subsystems of machine 110. For example, sensors 120 may monitorelectric characteristics, such as current flow and/or voltage potentialto determine whether an electronic subsystem is functioning normally.Sensors 120 may monitor any machine condition that is directly orindirectly indicative of the operability of machine 110 or itssubsystems, including, but not limited to, pressure, density, emissionsdata, speed, fluid level, fluid flow, volume flow rate, vibration,torque, force, throttle position, mass air-fuel ratio, traction, rotaryposition, rotational motion, and speed. Data collection system 115 mayinclude any combination of sensors 120 known in the art.

Data collection system 115 may include at least one local data system140, each local data system 140 being associated with one of machines110. Local data system 140 may embody a single microprocessor ormultiple microprocessors that include a means for receiving machine datafrom sensors 120 and/or other local data systems 140 and forcommunicating with other systems. Numerous commercially availablemicroprocessors can be configured to perform the functions of local datasystem 140. It should be appreciated that local data system 140 couldreadily embody a general machine or engine microprocessor capable ofgathering machine data. Local data system 140 may include all thecomponents required to run an application such as, for example, amemory, a secondary storage device, and a processor, such as a centralprocessing unit or any other means known. Various other known circuitsmay be associated with local data system 140, including power sourcecircuitry (not shown) and other appropriate circuitry.

In FIG. 2, each machine 110 in fleet 100 includes at least one localdata system 140, and local data system 140 may be associated with (e.g.,located within) machine 110. Local data system 140 may be configured toreceive machine data from the associated machine 110. For example, localdata system 140 may receive machine data from one or more of sensors 120associated with the same machine 110. Machine data may include anyinformation related to the operation or the condition of machine 110,including machine conditions measured and/or reported by sensors 120.

According to some embodiments, local data system 140 may continuallyoverwrite machine data received from sensors 120 with new machine data.Some embodiments of local data system 140 may be configured to store themost recent machine data, such as, for example, data from a predefinedperiod of time such as the last thirty seconds of operation. Byoverwriting outdated data to store recent machine data, the memory oflocal data system 140 can be smaller, as it will only store a predefinedmaximum amount of data from each sensor 120.

In some embodiments, local data system 140 may be configured to stopstoring and/or overwriting data from sensors 120 in response to atriggering event. This prevents overwriting data that may be useful indiagnosing the cause of the triggering events. According to someembodiments, local data system 140 may collect event-specific data inresponse to a triggering event. For example, once local data system 140detects a triggering event, local data system 140 may be configured tocontinue to collect data from sensors 120 for a predefined period oftime, such as for ten seconds after the triggering event is detected.Data collected after the triggering event that is detected may be called“event-specific data.” According to some embodiments, event-specificdata may be collected at a faster rate than sensors 120 collect dataprior to identifying a triggering condition. For example, sensors 120may collect event-specific data at 5 millisecond intervals.

Local data system 140 may be capable of detecting an event affectingmachine 110 based on machine data that local data system 140 receivesrelated to its associated machine 110. Once the occurrence of an eventis detected, local data system 140 may determine which portions of thecollected machine data are relevant data based on the event. Forexample, the relevant data portion may depend upon the nature of theevent. According to some embodiments, if the triggering event is anoverheating condition, the relevant data portion may include temperaturedata that local data system 140 has received from one or more of sensors120. Alternatively or additionally, the relevant data portion may dependon the timing of the triggering event. For example, if the event occurswithin a predefined time of machine startup, the relevant data portionmay include data related to the start up, including data related tosubsystems that had been operating at the time of the triggering eventand data related to the operability of those subsystems. Local datasystem 140 may identify the relevant data by the sensor 120 from whichthe data originates.

Data collection system 115 may also include a central data system 150.Central data system 150 may be associated with (e.g., located on) one ofthe plurality of machines 110. Alternatively, central data system 150may be located remotely with respect to machines 110. Central datasystem 150 may embody a single microprocessor or multiplemicroprocessors that include a means for receiving relevant data fromand/or for sending instructions to local data systems 140. Central datasystem 150 may be configured to receive machine data from at least oneof the plurality of local data systems 140. Central data system 150 mayalso be configured to analyze and/or process the machine data todetermine the cause of or solution to the triggering event. Numerouscommercially available microprocessors can be configured to perform thefunctions of central data system 150. It should be appreciated thatcentral data system 150 could readily embody a general machine or enginemicroprocessor capable of gathering machine data. Central data system150 may include all the components required to run an application suchas, for example, a memory, a secondary storage device, and a processor,such as a central processing unit or any other means known. Variousother known circuits may be associated with central data system 150,including power source circuitry (not shown) and other appropriatecircuitry.

Central data system 150 may be configured to receive the relevant dataportion from at least one of the plurality of local data systems 140.Once central data system 150 has received the relevant data portion,this data may be analyzed and/or processed (e.g., by central data system150) to determine the cause or solution of the triggering event.According to some embodiments, central data system 150 may receive therelevant data portion from all machines 110 in fleet 100 at the sametime. Alternatively, central data system 150 may receive the relevantdata portion from the affected machine 110 and then request the relevantdata portion from other machines 110. The order of data receipt may beconfigured by the user according to the particular application.

Local data system 140 may be configured to transmit the relevant dataportion and the event-specific data to central data system 150.According to some embodiments, local data system 140 may transmitevent-specific data to central data system 150 automatically.Alternatively, local data system 140 may transmit the event-specificdata to central data system 150 in response to a request from centraldata system 150. In a similar manner, local data system 140 may transmitthe relevant data portion to central data system 150 based on thetriggering event. Alternatively, central data system 150 may identifythe relevant data portion and communicate this to local data system 140.For example, central data system 150 may identify the relevant dataportion by the sensor(s) 120 from which the data originated.

For example, central data system 150 may be further configured toreceive a first signal from one or more of local data systems 140indicative of the nature of the event. In response, central data system150 may send a second signal to local data system 140 identifying theevent-specific data and requesting local data system 140 to begincollecting the event-specific data to transmit to central data system150. The second signal may also identify the relevant data portion, suchthat local data system 140 is able to identify and collect the relevantdata portion for central data system 150.

Central data system 150 may share information among different machines110. For example, central data system 150 may be configured to identifya plurality of machines 110 that share one or more characteristics withother machines 110. According to some embodiments, the plurality ofmachines 110 sharing one or more characteristics may make up a portionor all of machine fleet 100. In response to a triggering event in onemachine 110 of a particular machine fleet 100, central data system 150may be configured to communicate the identity of the relevant dataportion to other local data systems 140 in machine fleet 100. Centraldata system 150 may receive the relevant data portion from each of thelocal data systems 140 in machine fleet 100.

According to some embodiments, when a triggering event occurs on onemachine 110, local data system 140 not associated with that machine maybe configured to collect event-specific data. For example, central datasystem 150 may be configured to send a signal to all local data systems140 in a particular machine fleet 100 indicative of the occurrence of anevent on at least one machine 110 of machine fleet 100. Likewise,central data system 150 may request event-specific data from allmachines 110 in response to an event associated with one machine 110.

According to some embodiments, once local data system 140 has determinedthat a second event has occurred, it may be configured to collectevent-specific data and transmit this data to central data system 150.Central data system 150 may be configured to receive the event-specificdata and the relevant data portion from each machine 110 in machinefleet 100. Central data system 150 may analyze event-specific data fromall machines 110 in machine fleet 100 to determine any anomaliesparticular to machine 110 on which the event occurred to narrow down thecause of or solution to the event.

FIG. 3 is a flowchart of an exemplary method for collecting data from atleast one machine 110. For example, at step 200, the method may includedetecting an event associated with an affected machine 110 of the atleast one machine 110. Once the event has been detected, step 210 mayinclude determining a relevant data portion of machine data based on theevent. According to some embodiments, local data system 140 maydetermine the relevant data portion. Alternatively or additionally,central data system 150 may send a first signal to local data system 140of the affected machine identifying the relevant data portion. Centraldata system 150 may also send the first signal to other machines 110identified as having a common characteristic with the affected machine110.

According to some embodiments, central data system 150 may commandaffected machine 110 to collect event-specific data at step 220.According to some embodiments, central data system 150 may communicatethis command to the local data system 140 associated with the affectedmachine 110. Central data system 150 may also request event-specificdata from other machines 110 identified as having a commoncharacteristic with the affected machine 110. At step 230, central datasystem 150 may receive the event-specific data and the relevant dataportion. Optionally, central data system 150 may receive similar datafrom other machines 110 in machine fleet 100.

INDUSTRIAL APPLICABILITY

The disclosed systems and methods may provide a robust solution formaintenance and diagnostics of a fleet of machines. As a result ofevent-based data collection, the disclosed systems and methods mayprovide a more refined solution to data collection, decreasing theamount of unnecessary data transmission.

The presently disclosed systems and methods may have several advantages.For example, the disclosed data collection methods may more accuratelydiagnose a problem by collecting data related to the particular event.This solution may allow for customized data collection in response to aparticular fault or triggering event, which helps collect more data thatis likely to provide the key to correcting the identified fault.

Furthermore, the disclosed systems and methods may provide a moreefficient solution for data communication, which may be particularlyuseful when time spent transmitting or processing data may result inadditional downtime. For machines that are unable to operate until afault is corrected, this downtime may be costly and prevent operatorsfrom meeting deadlines. While more sensors are incorporated into complexmachines to monitor their operation, the key issue may no longer becollecting enough information to identify the problem. Rather, the keyto efficient repair of machine fleets may be identifying what portions,if any, of the gathered operational data are useful in solving aparticular problem.

Additionally, by gathering relevant data from all machines in the fleet,rather than just those suffering from a fault, the systems and methodsmay facilitate identifying a cause, rather than just a solution, ofparticular faults. For example, collecting event-specific and relevantmachine data from all machines in a fleet may help an engineer determinewhich values are particular to the broken machine and whichcharacteristics are within a normal range for that machine. Furthermore,sharing known problems among a fleet can help identify likely problemsof a particular machine within the fleet before that machine data isanalyzed.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the data collection systemsand associated methods for operating the same. Other embodiments of thepresent disclosure will be apparent to those skilled in the art fromconsideration of the specification and practice of the presentdisclosure. It is intended that the specification and examples beconsidered as exemplary only, with a true scope of the presentdisclosure being indicated by the following claims and theirequivalents.

1-8. (canceled)
 9. A processor-implemented method for collecting datafrom at least one machine, the method comprising: detecting via a firstprocessor an event associated with an affected machine of the at leastone machine; determining a relevant data portion of machine data basedon the event; commanding the affected machine to collect event-specificdata; and receiving via a second processor the event-specific data andthe relevant data portion from the affected machine.
 10. The method ofclaim 9, further including sending a first signal to the affectedmachine identifying the relevant data portion.
 11. The method of claim10, further including: identifying a plurality of machines that share acharacteristic with the at least one machine; sending the first signalto each of the plurality of machines identifying the relevant dataportion; and receiving the relevant data portions from each of theplurality of machines.
 12. The method of claim 11, further including:commanding each of the plurality of machines to collect theevent-specific data; and receiving the event-specific data from each ofthe plurality of machines.
 13. The method of claim 11, wherein theshared characteristic includes at least one of a machine type, a machinemodel, a working condition, a geographic location, and an environmentalcondition.
 14. The method of claim 9, wherein the event includes asystem fault of the affected machine. 15-20. (canceled)